Swarms offer a compelling substrate for reservoir computing, where agents interact through local rules while continuously rewiring their effective connectivity. We revisit swarm-based reservoirs with a focus on temporal memory, and the impact of adding a simple internal state system to agents. Rather than emphasizing single-task forecasting, our contribution is a clear, reproducible characterization of the swarm reservoir’s temporal memory and its scaling behavior, together with a practical implementation recipe compatible with graphics processing unit (GPU) acceleration. This positions multi-agent collectives as physically embodied alternatives to canonical neural reservoirs and clarifies when and why they are likely to be useful. Under a pure memory capacity (MC) protocol (linear readout, no polynomial expansion), the implemented two-state architecture of state-conditioned interaction parameters together with the switching logic used in this work yields memory that is roughly two orders of magnitude higher than single-state swarms at matched N (e. g. , at swarm size N = 1, 600: MC > 20 vs. ≈ 0. 1). With this two-state architecture present, the swarm’s total MC then scales linearly with population over N = 800–2, 000 (MC ≈ 0. 0123 N + 1. 61), robust to moderate process noise; a merged pure-MC fit over N = 2–2, 000 confirms the same trend (MC ≈ 0. 0134 N + 0. 92), indicating the effect is intrinsic to the swarm dynamics rather than a post-processing artifact. For context, a canonical neural reservoir exhibits the expected increase of memory with dimensionality. Finally, one-step chaotic prediction reveals a trade-off: single-state swarms excel at instantaneous prediction while multi-state swarms excel at temporal memory.
Lund et al. (Mon,) studied this question.